What is he best known for?
The fields of study he is best known for:
- Mechanics
- Fluid dynamics
- Thermodynamics
S. Balachandar mainly focuses on Mechanics, Classical mechanics, Reynolds number, Turbulence and Vortex.
Drag, Drag coefficient, Hele-Shaw flow, Direct numerical simulation and Compressibility are the subjects of his Mechanics studies.
His Classical mechanics research is multidisciplinary, incorporating perspectives in Flow, Open-channel flow, Vorticity and Vortex shedding.
His Reynolds number study incorporates themes from Point particle, Wake, Rotation and Magnetosphere particle motion.
S. Balachandar has researched Turbulence in several fields, including Geometry and Buoyancy.
His research integrates issues of Grashof number, Flow, Boussinesq approximation and Breakup in his study of Vortex.
His most cited work include:
- Mechanisms for generating coherent packets of hairpin vortices in channel flow (1470 citations)
- Turbulent Dispersed Multiphase Flow (926 citations)
- On the relationships between local vortex identification schemes (614 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mechanics, Classical mechanics, Turbulence, Reynolds number and Particle.
S. Balachandar works mostly in the field of Mechanics, limiting it down to topics relating to Shock and, in certain cases, Shock wave, as a part of the same area of interest.
His Classical mechanics study integrates concerns from other disciplines, such as Gravity current, Two-phase flow, Flow velocity, Vorticity and Open-channel flow.
S. Balachandar works mostly in the field of Turbulence, limiting it down to concerns involving Settling and, occasionally, Sediment and Geotechnical engineering.
His studies deal with areas such as Wake and Inviscid flow as well as Reynolds number.
His Particle research includes elements of Multiphase flow and Eulerian path.
He most often published in these fields:
- Mechanics (68.49%)
- Classical mechanics (27.35%)
- Turbulence (26.91%)
What were the highlights of his more recent work (between 2018-2021)?
- Mechanics (68.49%)
- Particle (16.85%)
- Turbulence (26.91%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mechanics, Particle, Turbulence, Multiphase flow and Reynolds number.
He has included themes like Dynamics and Shock in his Mechanics study.
His Particle research includes themes of Richtmyer–Meshkov instability and Shock tube.
His research investigates the connection between Turbulence and topics such as Immersed boundary method that intersect with issues in Bedform.
The concepts of his Reynolds number study are interwoven with issues in Point particle, Machine learning and Artificial intelligence.
His work deals with themes such as Vortex and Vorticity, which intersect with Shock wave.
Between 2018 and 2021, his most popular works were:
- Self-induced velocity correction for improved drag estimation in Euler-Lagrange point-particle simulations (36 citations)
- Host-to-Host Airborne Transmission As a Multiphase Flow Problem For Science-Based Social Distance Guidelines (27 citations)
- A hybrid point-particle force model that combines physical and data-driven approaches (20 citations)
In his most recent research, the most cited papers focused on:
- Mechanics
- Fluid dynamics
- Thermodynamics
S. Balachandar mostly deals with Mechanics, Reynolds number, Turbulence, Multiphase flow and Particle.
His study of Flow is a part of Mechanics.
His work carried out in the field of Reynolds number brings together such families of science as Vortex tube, Mathematical analysis, Laminar flow, Point particle and Drag.
His Turbulence research incorporates elements of Immersed boundary method, Streamlines, streaklines, and pathlines, Buoyancy, Gravity and Bedform.
His studies in Multiphase flow integrate themes in fields like Range, Fluidization, Euler lagrange and Rotational symmetry.
His Particle study combines topics from a wide range of disciplines, such as Eulerian path, Data-driven, Lift and Regression.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
